Joint Latent Topic Discovery and Expectation Modeling for Financial Markets

• URL: http://arxiv.org/abs/2307.08649v1
• Date: Thu, 1 Jun 2023 01:36:51 GMT
• Title: Joint Latent Topic Discovery and Expectation Modeling for Financial Markets
• Authors: Lili Wang, Chenghan Huang, Chongyang Gao, Weicheng Ma, and Soroush Vosoughi
• Abstract summary: We present a groundbreaking framework for financial market analysis. This approach is the first to jointly model investor expectations and automatically mine latent stock relationships. Our model consistently achieves an annual return exceeding 10%.
• Score: 45.758436505779386
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In the pursuit of accurate and scalable quantitative methods for financial market analysis, the focus has shifted from individual stock models to those capturing interrelations between companies and their stocks. However, current relational stock methods are limited by their reliance on predefined stock relationships and the exclusive consideration of immediate effects. To address these limitations, we present a groundbreaking framework for financial market analysis. This approach, to our knowledge, is the first to jointly model investor expectations and automatically mine latent stock relationships. Comprehensive experiments conducted on China's CSI 300, one of the world's largest markets, demonstrate that our model consistently achieves an annual return exceeding 10%. This performance surpasses existing benchmarks, setting a new state-of-the-art standard in stock return prediction and multiyear trading simulations (i.e., backtesting).

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